Identification of tropical cyclones via deep convolutional neural network based on satellite cloud images

Tong, Biao; Sun, Xiangfei; Fu, Jiyang; He, Yuncheng; Chan, Pakwai

doi:https://doi.org/10.5194/amt-15-1829-2022

Articles | Volume 15, issue 6

https://doi.org/10.5194/amt-15-1829-2022

© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/amt-15-1829-2022

© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 15, issue 6

Research article

|

25 Mar 2022

Research article |

| 25 Mar 2022

Identification of tropical cyclones via deep convolutional neural network based on satellite cloud images

Biao Tong, Xiangfei Sun, Jiyang Fu, Yuncheng He, and Pakwai Chan

Supplement

https://doi.org/10.5194/amt-15-1829-2022-supplement

Data sets

An overview of the China Meteorological Administration tropical cyclone database (http://tcdata.typhoon.org.cn/) M. Ying, W. Zhang, H. Yu, X. Lu, J. Feng, Y. Fan, Y. Zhu, and D. Chen https://doi.org/10.1175/JTECH-D-12-00119.1

Western north pacific tropical cyclone database created by the China meteorological administration (http://tcdata.typhoon.org.cn/) X. Lu, H. Yu, M. Ying, B. Zhao, S. Zhang, L. Lin, L. Bai, and R. Wan https://doi.org/10.1007/s00376-020-0211-7

Short summary

In recent years, there has been numerous research on tropical cyclone (TC) observation based on satellite cloud images (SCIs), but most methods are limited by low efficiency and subjectivity. To overcome subjectivity and improve efficiency of traditional methods, this paper uses deep learning technology to do further research on fingerprint identification of TCs. Results provide an automatic and objective method to distinguish TCs from SCIs and are convenient for subsequent research.